



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType







class 				_a				(   _lowerCAmelCase					):

			def __init__(self,   SCREAMING_SNAKE_CASE_					)       ->   int:
						UpperCAmelCase_:		List[str]							      =     data

			def __iter__(self					)       ->   int:
						for element in self.data:
									yield element







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[int]=True  ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]							      =     Accelerator(even_batches=lowerCAmelCase__  )
			assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
			return accelerator







def  lowerCAmelCase_	(lowerCAmelCase__:		Accelerator		,						lowerCAmelCase__:		int		,						lowerCAmelCase__:		int		,						lowerCAmelCase__:		bool = False  ):





			"""simple docstring"""


			if iterable:
						UpperCAmelCase_:		Tuple							      =     DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase__  )  )  )
			else:
						UpperCAmelCase_:		Tuple							      =     TensorDataset(torch.as_tensor(range(lowerCAmelCase__  )  )  )

			UpperCAmelCase_:		str							      =     DataLoader(lowerCAmelCase__		,						batch_size=lowerCAmelCase__  )
			UpperCAmelCase_:		Tuple							      =     accelerator.prepare(lowerCAmelCase__  )

			return dl







def  lowerCAmelCase_	(lowerCAmelCase__:		Accelerator		,						lowerCAmelCase__:		int		,						lowerCAmelCase__:		int		,						lowerCAmelCase__:		List[int]		,						lowerCAmelCase__:		List[int]		,						):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     create_dataloader(accelerator=lowerCAmelCase__		,						dataset_size=lowerCAmelCase__		,						batch_size=lowerCAmelCase__  )

			UpperCAmelCase_:		Optional[Any]							      =     [len(batch[0]  ) for batch in dl]

			if accelerator.process_index == 0:
						assert batch_sizes == process_0_expected_batch_sizes
			elif accelerator.process_index == 1:
						assert batch_sizes == process_1_expected_batch_sizes







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Optional[Any]							      =     create_accelerator()

			# without padding, we would expect a different number of batches
			verify_dataloader_batch_sizes(
			    lowerCAmelCase__		,						dataset_size=3		,						batch_size=1		,						process_0_expected_batch_sizes=[1, 1]		,						process_1_expected_batch_sizes=[1, 1]		,						)

			# without padding, we would expect the same number of batches, but different sizes
			verify_dataloader_batch_sizes(
			    lowerCAmelCase__		,						dataset_size=7		,						batch_size=2		,						process_0_expected_batch_sizes=[2, 2]		,						process_1_expected_batch_sizes=[2, 2]		,						)







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     create_accelerator(even_batches=lowerCAmelCase__  )

			verify_dataloader_batch_sizes(
			    lowerCAmelCase__		,						dataset_size=3		,						batch_size=1		,						process_0_expected_batch_sizes=[1, 1]		,						process_1_expected_batch_sizes=[1]		,						)

			verify_dataloader_batch_sizes(
			    lowerCAmelCase__		,						dataset_size=7		,						batch_size=2		,						process_0_expected_batch_sizes=[2, 2]		,						process_1_expected_batch_sizes=[2, 1]		,						)







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Union[str, Any]							      =     create_accelerator(even_batches=lowerCAmelCase__  )

			UpperCAmelCase_:		Optional[int]							      =     torch.nn.Linear(1		,						1  )
			UpperCAmelCase_:		Union[str, Any]							      =     accelerator.prepare(lowerCAmelCase__  )

			UpperCAmelCase_:		Tuple							      =     create_dataloader(lowerCAmelCase__		,						dataset_size=3		,						batch_size=1  )

			UpperCAmelCase_:		int							      =     []
			with accelerator.join_uneven_inputs([ddp_model]  ):
						for batch_idx, batch in enumerate(lowerCAmelCase__  ):
									UpperCAmelCase_:		Any							      =     ddp_model(batch[0].float()  )
									UpperCAmelCase_:		Any							      =     output.sum()
									loss.backward()
									batch_idxs.append(lowerCAmelCase__  )

			accelerator.wait_for_everyone()

			if accelerator.process_index == 0:
						assert batch_idxs == [0, 1]
			elif accelerator.process_index == 1:
						assert batch_idxs == [0]







def  lowerCAmelCase_	(lowerCAmelCase__:		str  ):





			"""simple docstring"""


			with warnings.catch_warnings(record=lowerCAmelCase__  ) as w:
						with accelerator.join_uneven_inputs([Mock()]  ):
									pass

						assert issubclass(w[-1].category		,						lowerCAmelCase__  )
						assert "only supported for multi-GPU" in str(w[-1].message  )







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]							      =     True
			UpperCAmelCase_:		Union[str, Any]							      =     False
			UpperCAmelCase_:		Optional[Any]							      =     create_accelerator(even_batches=lowerCAmelCase__  )
			UpperCAmelCase_:		str							      =     torch.nn.Linear(1		,						1  )
			UpperCAmelCase_:		int							      =     accelerator.prepare(lowerCAmelCase__  )
			UpperCAmelCase_:		int							      =     create_dataloader(lowerCAmelCase__		,						dataset_size=3		,						batch_size=1  )
			UpperCAmelCase_:		List[Any]							      =     create_dataloader(lowerCAmelCase__		,						dataset_size=3		,						batch_size=1  )

			with accelerator.join_uneven_inputs([ddp_model]		,						even_batches=lowerCAmelCase__  ):
						UpperCAmelCase_:		str							      =     train_dl.batch_sampler.even_batches
						UpperCAmelCase_:		Dict							      =     valid_dl.batch_sampler.even_batches

			assert train_dl_overridden_value == overridden_even_batches
			assert valid_dl_overridden_value == overridden_even_batches
			assert train_dl.batch_sampler.even_batches == default_even_batches
			assert valid_dl.batch_sampler.even_batches == default_even_batches







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]							      =     True
			UpperCAmelCase_:		Tuple							      =     False
			UpperCAmelCase_:		int							      =     create_accelerator(even_batches=lowerCAmelCase__  )
			UpperCAmelCase_:		str							      =     torch.nn.Linear(1		,						1  )
			UpperCAmelCase_:		Optional[int]							      =     accelerator.prepare(lowerCAmelCase__  )
			create_dataloader(lowerCAmelCase__		,						dataset_size=3		,						batch_size=1		,						iterable=lowerCAmelCase__  )
			UpperCAmelCase_:		Dict							      =     create_dataloader(lowerCAmelCase__		,						dataset_size=3		,						batch_size=1  )

			with warnings.catch_warnings():
						warnings.filterwarnings("""ignore"""  )
						try:
									with accelerator.join_uneven_inputs([ddp_model]		,						even_batches=lowerCAmelCase__  ):
												UpperCAmelCase_:		List[Any]							      =     batch_dl.batch_sampler.even_batches
						except AttributeError:
									# ensure attribute error is not raised when processing iterable dl
									raise AssertionError

			assert batch_dl_overridden_value == overridden_even_batches
			assert batch_dl.batch_sampler.even_batches == default_even_batches







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     create_accelerator()
			UpperCAmelCase_:		Dict							      =     torch.nn.Linear(1		,						1  )
			UpperCAmelCase_:		str							      =     accelerator.prepare(lowerCAmelCase__  )
			create_dataloader(lowerCAmelCase__		,						dataset_size=3		,						batch_size=1		,						iterable=lowerCAmelCase__  )

			with warnings.catch_warnings(record=lowerCAmelCase__  ) as w:
						with accelerator.join_uneven_inputs([ddp_model]		,						even_batches=lowerCAmelCase__  ):
									pass

						assert issubclass(w[-1].category		,						lowerCAmelCase__  )
						assert "only supported for map-style datasets" in str(w[-1].message  )







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     create_accelerator()

			accelerator.print("""Test that even_batches variable ensures uniform batches across processes"""  )
			test_default_ensures_even_batch_sizes()

			accelerator.print("""Run tests with even_batches disabled"""  )
			test_can_disable_even_batches()

			accelerator.print("""Test joining uneven inputs"""  )
			test_can_join_uneven_inputs()

			accelerator.print("""Test overriding even_batches when joining uneven inputs"""  )
			test_join_can_override_even_batches()

			accelerator.print("""Test overriding even_batches for mixed dataloader types"""  )
			test_join_can_override_for_mixed_type_dataloaders()

			accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders"""  )
			test_join_raises_warning_for_iterable_when_overriding_even_batches()

			accelerator.print("""Test join with non DDP distributed raises warning"""  )
			UpperCAmelCase_:		Optional[int]							      =     accelerator.state.distributed_type
			UpperCAmelCase_:		Optional[Any]							      =     DistributedType.FSDP
			test_join_raises_warning_for_non_ddp_distributed(lowerCAmelCase__  )
			UpperCAmelCase_:		Union[str, Any]							      =     original_state


if __name__ == "__main__":
	main()











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a      :  List[str]			     =			logging.get_logger(__name__)


if is_vision_available():
	import PIL







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''pixel_values''']

			def __init__(self,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = 1 / 255,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = True,   **SCREAMING_SNAKE_CASE_,   )       ->   None:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     size if size is not None else {"""shortest_edge""": 224}
						UpperCAmelCase_:		int							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   default_to_square=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						UpperCAmelCase_:		Union[str, Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   default_to_square=SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		Optional[int]							      =     do_resize
						UpperCAmelCase_:		Optional[int]							      =     size
						UpperCAmelCase_:		Optional[int]							      =     resample
						UpperCAmelCase_:		Tuple							      =     do_center_crop
						UpperCAmelCase_:		Optional[int]							      =     crop_size
						UpperCAmelCase_:		str							      =     do_rescale
						UpperCAmelCase_:		Optional[int]							      =     rescale_factor
						UpperCAmelCase_:		List[str]							      =     do_normalize
						UpperCAmelCase_:		Any							      =     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						UpperCAmelCase_:		Dict							      =     image_std if image_std is not None else OPENAI_CLIP_STD
						UpperCAmelCase_:		Any							      =     do_convert_rgb

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   default_to_square=SCREAMING_SNAKE_CASE_					)
						if "shortest_edge" not in size:
									raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'					)
						UpperCAmelCase_:		Optional[Any]							      =     get_resize_output_image_size(SCREAMING_SNAKE_CASE_,   size=size["""shortest_edge"""],   default_to_square=SCREAMING_SNAKE_CASE_					)
						return resize(SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Any							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'					)
						return center_crop(SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   Union[str, Any]:
						return rescale(SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						return normalize(SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,   **SCREAMING_SNAKE_CASE_,   )       ->   PIL.Image.Image:
						UpperCAmelCase_:		Tuple							      =     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_:		Tuple							      =     size if size is not None else self.size
						UpperCAmelCase_:		str							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""size""",   default_to_square=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     resample if resample is not None else self.resample
						UpperCAmelCase_:		Optional[int]							      =     do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCAmelCase_:		Tuple							      =     crop_size if crop_size is not None else self.crop_size
						UpperCAmelCase_:		int							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size""",   default_to_square=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Union[str, Any]							      =     do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase_:		int							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase_:		Tuple							      =     do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_:		Dict							      =     image_mean if image_mean is not None else self.image_mean
						UpperCAmelCase_:		int							      =     image_std if image_std is not None else self.image_std
						UpperCAmelCase_:		Union[str, Any]							      =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						UpperCAmelCase_:		List[str]							      =     make_list_of_images(SCREAMING_SNAKE_CASE_					)

						if not valid_images(SCREAMING_SNAKE_CASE_					):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

						if do_resize and size is None:
									raise ValueError("""Size must be specified if do_resize is True."""					)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
									UpperCAmelCase_:		Any							      =     [convert_to_rgb(SCREAMING_SNAKE_CASE_					) for image in images]

						# All transformations expect numpy arrays.
						UpperCAmelCase_:		List[str]							      =     [to_numpy_array(SCREAMING_SNAKE_CASE_					) for image in images]

						if do_resize:
									UpperCAmelCase_:		str							      =     [self.resize(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_center_crop:
									UpperCAmelCase_:		Dict							      =     [self.center_crop(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_rescale:
									UpperCAmelCase_:		Optional[Any]							      =     [self.rescale(image=SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_normalize:
									UpperCAmelCase_:		Dict							      =     [self.normalize(image=SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Tuple							      =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Union[str, Any]							      =     {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_,   tensor_type=SCREAMING_SNAKE_CASE_					)






